Overview

Dataset statistics

Number of variables36
Number of observations28322
Missing cells135776
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.7 MiB
Average record size in memory1.1 KiB

Variable types

Categorical18
Boolean2
Unsupported3
Numeric13

Alerts

id has a high cardinality: 28322 distinct values High cardinality
n1 is highly correlated with n2 and 3 other fieldsHigh correlation
n2 is highly correlated with n1 and 3 other fieldsHigh correlation
n3 is highly correlated with n7High correlation
n7 is highly correlated with n1 and 4 other fieldsHigh correlation
n8 is highly correlated with n1 and 2 other fieldsHigh correlation
n10 is highly correlated with n1 and 2 other fieldsHigh correlation
n1 is highly correlated with n2 and 2 other fieldsHigh correlation
n2 is highly correlated with n1 and 3 other fieldsHigh correlation
n3 is highly correlated with n7 and 1 other fieldsHigh correlation
n7 is highly correlated with n1 and 4 other fieldsHigh correlation
n8 is highly correlated with n1 and 2 other fieldsHigh correlation
n10 is highly correlated with n2 and 2 other fieldsHigh correlation
n1 is highly correlated with n2High correlation
n2 is highly correlated with n1High correlation
n7 is highly correlated with n10High correlation
n10 is highly correlated with n7High correlation
s70 is highly correlated with s17High correlation
s16 is highly correlated with s69High correlation
s71 is highly correlated with s18High correlation
s18 is highly correlated with s71High correlation
s69 is highly correlated with s16High correlation
s17 is highly correlated with s70High correlation
s11 is highly correlated with s18 and 1 other fieldsHigh correlation
s16 is highly correlated with s17 and 6 other fieldsHigh correlation
s17 is highly correlated with s16 and 4 other fieldsHigh correlation
s18 is highly correlated with s11 and 7 other fieldsHigh correlation
s52 is highly correlated with s16 and 4 other fieldsHigh correlation
s69 is highly correlated with s16 and 6 other fieldsHigh correlation
s70 is highly correlated with s16 and 4 other fieldsHigh correlation
s71 is highly correlated with s11 and 7 other fieldsHigh correlation
n1 is highly correlated with n2 and 6 other fieldsHigh correlation
n2 is highly correlated with n1 and 6 other fieldsHigh correlation
n3 is highly correlated with n1 and 6 other fieldsHigh correlation
n5 is highly correlated with n6High correlation
n6 is highly correlated with n1 and 6 other fieldsHigh correlation
n7 is highly correlated with n1 and 6 other fieldsHigh correlation
n8 is highly correlated with n1 and 5 other fieldsHigh correlation
n9 is highly correlated with n1 and 4 other fieldsHigh correlation
n10 is highly correlated with n1 and 6 other fieldsHigh correlation
label is highly correlated with s16 and 4 other fieldsHigh correlation
s54 has 25694 (90.7%) missing values Missing
s55 has 25116 (88.7%) missing values Missing
s56 has 28322 (100.0%) missing values Missing
s57 has 28322 (100.0%) missing values Missing
s59 has 28322 (100.0%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
n14 has unique values Unique
s56 is an unsupported type, check if it needs cleaning or further analysis Unsupported
s57 is an unsupported type, check if it needs cleaning or further analysis Unsupported
s59 is an unsupported type, check if it needs cleaning or further analysis Unsupported
n15 has 4145 (14.6%) zeros Zeros

Reproduction

Analysis started2022-06-09 17:06:12.327293
Analysis finished2022-06-09 17:06:36.108046
Duration23.78 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct28322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
b'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='
 
1
b'gAAAAABinOiekMHaC6-03yzvmLFVBfqljUIev5XFrbbJarEbo-mshNj5pWAXfTdmvEMQtb0WjtlboHt2rFBrCoipjAn9sOOZEg=='
 
1
b'gAAAAABinOiYtx-HpOZBYTtT-FndzIj6Nt2HTKu7UKAs-Dfxd8mxrvwxRNGE47Si_-kc5TiwVkTf59u94658aAU7gHD9-TGBMQ=='
 
1
b'gAAAAABinOia961WRLsDIRdEcGr5_RSKfmTjQ2ME5HBpIUtBdjJUeCTgVI6uzDIdnDRB58VBUvirHgdTdjgECltfpZ2XequVmA=='
 
1
b'gAAAAABinOibb7LcG8T4rQSnzf-b2GjK5D0F1ZLe6VMES-x90Pi5-Emk_dbp7xYXuepQpmVa_rhfEsemOUZUGn9_30mt-Vr8iQ=='
 
1
Other values (28317)
28317 

Length

Max length103
Median length103
Mean length103
Min length103

Characters and Unicode

Total characters2917166
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28322 ?
Unique (%)100.0%

Sample

1st rowb'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='
2nd rowb'gAAAAABinOiWGC1WhR6WYP0DA5ssGv9rIekrWUwCdJ8FvkVcSUl2AquMfWqtOqs3AQYGxS13wQv9Tx4GEkPEl5RnbchazqsZcw=='
3rd rowb'gAAAAABinOibTcOBFIVeA4nVF3FuFz_QX3ZlPPFc21gS9EYdw6Wo8Y5agbzfD6hhsaXZCBdrUQVPpZBXYsODc2PDjER2DX5QcA=='
4th rowb'gAAAAABinOig-g3-Q1ggjlMhfUSdn21Aj5yVVeVvXbisuGUmadvbBh5W28jivd2vgGUWVfHtMdC6vNLrDyFM5NgzILAOorgWGA=='
5th rowb'gAAAAABinOiXdoaNUzihOSbyY1tjWtd5EgMaXkkvH6SVbyppsCh4sW4X5QGqFrLNAcfMQ4NPHOLqbNUVKU-5xxvWCwb5tT91Pw=='

Common Values

ValueCountFrequency (%)
b'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='1
 
< 0.1%
b'gAAAAABinOiekMHaC6-03yzvmLFVBfqljUIev5XFrbbJarEbo-mshNj5pWAXfTdmvEMQtb0WjtlboHt2rFBrCoipjAn9sOOZEg=='1
 
< 0.1%
b'gAAAAABinOiYtx-HpOZBYTtT-FndzIj6Nt2HTKu7UKAs-Dfxd8mxrvwxRNGE47Si_-kc5TiwVkTf59u94658aAU7gHD9-TGBMQ=='1
 
< 0.1%
b'gAAAAABinOia961WRLsDIRdEcGr5_RSKfmTjQ2ME5HBpIUtBdjJUeCTgVI6uzDIdnDRB58VBUvirHgdTdjgECltfpZ2XequVmA=='1
 
< 0.1%
b'gAAAAABinOibb7LcG8T4rQSnzf-b2GjK5D0F1ZLe6VMES-x90Pi5-Emk_dbp7xYXuepQpmVa_rhfEsemOUZUGn9_30mt-Vr8iQ=='1
 
< 0.1%
b'gAAAAABinOibK_rkQizw2v7YY3szuGFOgZwrQMh0YPrLzI3o7dQCle4QK9ETVtfScASiIDtHwQHBd7x27Dgeea_UmaJoGpfm0A=='1
 
< 0.1%
b'gAAAAABinOidsgNx-htIilWXrDZ-K1KBZ6KiOx7-KA1TasAjgz_Jy_gi8h0nZR-LrWUWFmVjR5IDSPro-mQRZIrySb3QZz9h_w=='1
 
< 0.1%
b'gAAAAABinOiZKkM1JKiW0X9Z65ZZmaWuqgFhAmcYv2ho5NTz34exXuo3I9RC6foBf_LU4E3oxVW3YjTZjyuGY1OLM3lJemZNvA=='1
 
< 0.1%
b'gAAAAABinOiWZF3p2zrzeyg5boURFiJZl-JpmflTqjHS0fEyrh0heA1UsZbF2x5BSXMR9kHj65sDK7kgiQtYCB-v8x0_R9fZ4Q=='1
 
< 0.1%
b'gAAAAABinOifWDvv56Hrsliu-h56I5XJ2DDmNPHit6geidQjo-asG1fO_xwDiXJuLMqbHLze2DzoUEYUzhMcNV8a1VIHrpXr4w=='1
 
< 0.1%
Other values (28312)28312
> 99.9%

Length

2022-06-09T23:06:36.144048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b'gaaaaabinoics09vrmgh0_jyehihi13pto0rcyhp7l76be71pwa2reuc4huqn16fya1z8_vstnnfgaxjf262cgsumpzoaknseg1
 
< 0.1%
b'gaaaaabinoizfrale3zmaqhl1ux4ffui5lz9r-gkrk06cjdh_rkebne5j6ayf0un3gdpfgshwrn1xaiiniupizjnmkloolieaa1
 
< 0.1%
b'gaaaaabinoiyfrgmhzu5slaayktwjbrhpnjcypvxqpnfcegg_-eygdg_9f43uyqqd9ok9mkzlmuyqmf9ly0pk0gozfdqzbfmia1
 
< 0.1%
b'gaaaaabinoixqjg5rjpzshsaqrjhp8u_21fwetw89efnxr9ftb8fh3plyl5ev8psgkzrz0gybpckfs3ldv44cbp12ftatgrtfw1
 
< 0.1%
b'gaaaaabinoic9rpk09lv7q2k7bmbzxst0zlua8sft7x0zu92omuyqexuohsyx-ubx1jlsbooyn9pptykg6nas-hibfvbnv3wow1
 
< 0.1%
b'gaaaaabinoiwr-5mklxyj9hkimylh2xnhjeywiipt94iypkqenbqh9nezdgg27apxcq4nzhzsc8-sqrrxsggxemge3oslznzfw1
 
< 0.1%
b'gaaaaabinoiwojfncdi_4ynxirygki_rpyp_4lrn0qudm6b6ug2xeumlslet6b3jsfms1yp3wschxf0lqlgpla_mdzsh2xgiuq1
 
< 0.1%
b'gaaaaabinoieinoucnx-mhyzbo0_dldtmh1cb7opn_mncdiqiryjivxswhwugykxfd87triclgcsjjtzemml9anbpsyt9iusza1
 
< 0.1%
b'gaaaaabinoid6obuufesrjiycahsi4zryczn53dwd9qqdri7wrujd5cxyhipvrzgbddy1fmivzvbtn4n4pbgojej2abjimfpqq1
 
< 0.1%
b'gaaaaabinoizn9dh3ix0mez68xy7n84c4nbp_8trnlygwpztchz4idwdiwt9i3jrefbwmiq7biugodmgv2ludfkllvjrxq8qyq1
 
< 0.1%
Other values (28312)28312
> 99.9%

Most occurring characters

ValueCountFrequency (%)
A186667
 
6.4%
i94254
 
3.2%
g74948
 
2.6%
b68983
 
2.4%
B66029
 
2.3%
n65873
 
2.3%
O65851
 
2.3%
'56644
 
1.9%
=56644
 
1.9%
w44951
 
1.5%
Other values (56)2136322
73.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1203370
41.3%
Lowercase Letter1149556
39.4%
Decimal Number375924
 
12.9%
Other Punctuation56644
 
1.9%
Math Symbol56644
 
1.9%
Connector Punctuation37629
 
1.3%
Dash Punctuation37399
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A186667
 
15.5%
B66029
 
5.5%
O65851
 
5.5%
Q44665
 
3.7%
Z40592
 
3.4%
X40496
 
3.4%
Y40458
 
3.4%
W40079
 
3.3%
V39027
 
3.2%
I38064
 
3.2%
Other values (16)601442
50.0%
Lowercase Letter
ValueCountFrequency (%)
i94254
 
8.2%
g74948
 
6.5%
b68983
 
6.0%
n65873
 
5.7%
w44951
 
3.9%
a40259
 
3.5%
c39894
 
3.5%
d39858
 
3.5%
e39439
 
3.4%
f39317
 
3.4%
Other values (16)601780
52.3%
Decimal Number
ValueCountFrequency (%)
937882
10.1%
137832
10.1%
637773
10.0%
537678
10.0%
037580
10.0%
837571
10.0%
237566
10.0%
337493
10.0%
737433
10.0%
437116
9.9%
Other Punctuation
ValueCountFrequency (%)
'56644
100.0%
Math Symbol
ValueCountFrequency (%)
=56644
100.0%
Connector Punctuation
ValueCountFrequency (%)
_37629
100.0%
Dash Punctuation
ValueCountFrequency (%)
-37399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2352926
80.7%
Common564240
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A186667
 
7.9%
i94254
 
4.0%
g74948
 
3.2%
b68983
 
2.9%
B66029
 
2.8%
n65873
 
2.8%
O65851
 
2.8%
w44951
 
1.9%
Q44665
 
1.9%
Z40592
 
1.7%
Other values (42)1600113
68.0%
Common
ValueCountFrequency (%)
'56644
 
10.0%
=56644
 
10.0%
937882
 
6.7%
137832
 
6.7%
637773
 
6.7%
537678
 
6.7%
_37629
 
6.7%
037580
 
6.7%
837571
 
6.7%
237566
 
6.7%
Other values (4)149441
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2917166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A186667
 
6.4%
i94254
 
3.2%
g74948
 
2.6%
b68983
 
2.4%
B66029
 
2.3%
n65873
 
2.3%
O65851
 
2.3%
'56644
 
1.9%
=56644
 
1.9%
w44951
 
1.5%
Other values (56)2136322
73.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
M
20396 
F
7926 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M20396
72.0%
F7926
 
28.0%

Length

2022-06-09T23:06:36.213048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:36.279045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
m20396
72.0%
f7926
 
28.0%

Most occurring characters

ValueCountFrequency (%)
M20396
72.0%
F7926
 
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M20396
72.0%
F7926
 
28.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M20396
72.0%
F7926
 
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M20396
72.0%
F7926
 
28.0%

s11
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.8 KiB
True
25112 
False
3210 
ValueCountFrequency (%)
True25112
88.7%
False3210
 
11.3%
2022-06-09T23:06:36.339049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

s12
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.8 KiB
True
24227 
False
4095 
ValueCountFrequency (%)
True24227
85.5%
False4095
 
14.5%
2022-06-09T23:06:36.401046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

s13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
27844 
0
 
478

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

Length

2022-06-09T23:06:36.457648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:36.522653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

Most occurring characters

ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common28322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127844
98.3%
0478
 
1.7%

s16
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
D
21028 
B
6446 
C
 
540
A
 
308

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D21028
74.2%
B6446
 
22.8%
C540
 
1.9%
A308
 
1.1%

Length

2022-06-09T23:06:36.580654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:36.651655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
d21028
74.2%
b6446
 
22.8%
c540
 
1.9%
a308
 
1.1%

Most occurring characters

ValueCountFrequency (%)
D21028
74.2%
B6446
 
22.8%
C540
 
1.9%
A308
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D21028
74.2%
B6446
 
22.8%
C540
 
1.9%
A308
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D21028
74.2%
B6446
 
22.8%
C540
 
1.9%
A308
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D21028
74.2%
B6446
 
22.8%
C540
 
1.9%
A308
 
1.1%

s17
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
D
24013 
B
 
2175
C
 
1985
A
 
149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%

Length

2022-06-09T23:06:36.716654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:36.787655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
d24013
84.8%
b2175
 
7.7%
c1985
 
7.0%
a149
 
0.5%

Most occurring characters

ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%

s18
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
B
25010 
D
 
1612
C
 
1607
A
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
B25010
88.3%
D1612
 
5.7%
C1607
 
5.7%
A93
 
0.3%

Length

2022-06-09T23:06:36.849654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:36.920654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Most occurring characters

ValueCountFrequency (%)
B25010
88.3%
D1612
 
5.7%
C1607
 
5.7%
A93
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B25010
88.3%
D1612
 
5.7%
C1607
 
5.7%
A93
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B25010
88.3%
D1612
 
5.7%
C1607
 
5.7%
A93
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B25010
88.3%
D1612
 
5.7%
C1607
 
5.7%
A93
 
0.3%

s48
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
16049 
1
12273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
016049
56.7%
112273
43.3%

Length

2022-06-09T23:06:36.981654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.049654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
016049
56.7%
112273
43.3%

Most occurring characters

ValueCountFrequency (%)
016049
56.7%
112273
43.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016049
56.7%
112273
43.3%

Most occurring scripts

ValueCountFrequency (%)
Common28322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016049
56.7%
112273
43.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016049
56.7%
112273
43.3%

s52
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
20524 
l
6508 
0
 
1094
o
 
196

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th rowl

Common Values

ValueCountFrequency (%)
120524
72.5%
l6508
 
23.0%
01094
 
3.9%
o196
 
0.7%

Length

2022-06-09T23:06:37.109654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.181654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
120524
72.5%
l6508
 
23.0%
01094
 
3.9%
o196
 
0.7%

Most occurring characters

ValueCountFrequency (%)
120524
72.5%
l6508
 
23.0%
01094
 
3.9%
o196
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21618
76.3%
Lowercase Letter6704
 
23.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120524
94.9%
01094
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
l6508
97.1%
o196
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common21618
76.3%
Latin6704
 
23.7%

Most frequent character per script

Common
ValueCountFrequency (%)
120524
94.9%
01094
 
5.1%
Latin
ValueCountFrequency (%)
l6508
97.1%
o196
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120524
72.5%
l6508
 
23.0%
01094
 
3.9%
o196
 
0.7%

s53
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
25752 
 
2570

Length

Max length2
Median length2
Mean length1.909257821
Min length1

Characters and Unicode

Total characters54074
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
25752
90.9%
2570
 
9.1%

Length

2022-06-09T23:06:37.247655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.317656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
54074
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator54074
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
54074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common54074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
54074
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII54074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
54074
100.0%

s54
Categorical

MISSING

Distinct9
Distinct (%)0.3%
Missing25694
Missing (%)90.7%
Memory size954.5 KiB
2b
316 
b2
314 
a2
314 
22
310 
ab
297 
Other values (4)
1077 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5256
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb2
2nd rowaa
3rd rowb2
4th row22
5th row2a

Common Values

ValueCountFrequency (%)
2b316
 
1.1%
b2314
 
1.1%
a2314
 
1.1%
22310
 
1.1%
ab297
 
1.0%
2a291
 
1.0%
ba287
 
1.0%
bb260
 
0.9%
aa239
 
0.8%
(Missing)25694
90.7%

Length

2022-06-09T23:06:37.375655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.459656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2b316
12.0%
b2314
11.9%
a2314
11.9%
22310
11.8%
ab297
11.3%
2a291
11.1%
ba287
10.9%
bb260
9.9%
aa239
9.1%

Most occurring characters

ValueCountFrequency (%)
21855
35.3%
b1734
33.0%
a1667
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3401
64.7%
Decimal Number1855
35.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b1734
51.0%
a1667
49.0%
Decimal Number
ValueCountFrequency (%)
21855
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3401
64.7%
Common1855
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
b1734
51.0%
a1667
49.0%
Common
ValueCountFrequency (%)
21855
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21855
35.3%
b1734
33.0%
a1667
31.7%

s55
Categorical

MISSING

Distinct9
Distinct (%)0.3%
Missing25116
Missing (%)88.7%
Memory size969.7 KiB
2k
405 
2K
400 
k2
364 
K2
359 
22
345 
Other values (4)
1333 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6412
Distinct characters3
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkK
2nd rowkk
3rd row2K
4th rowk2
5th rowKK

Common Values

ValueCountFrequency (%)
2k405
 
1.4%
2K400
 
1.4%
k2364
 
1.3%
K2359
 
1.3%
22345
 
1.2%
Kk345
 
1.2%
KK343
 
1.2%
kk330
 
1.2%
kK315
 
1.1%
(Missing)25116
88.7%

Length

2022-06-09T23:06:37.549656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.641654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kk1333
41.6%
2k805
25.1%
k2723
22.6%
22345
 
10.8%

Most occurring characters

ValueCountFrequency (%)
22218
34.6%
K2105
32.8%
k2089
32.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2218
34.6%
Uppercase Letter2105
32.8%
Lowercase Letter2089
32.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22218
100.0%
Uppercase Letter
ValueCountFrequency (%)
K2105
100.0%
Lowercase Letter
ValueCountFrequency (%)
k2089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4194
65.4%
Common2218
34.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
K2105
50.2%
k2089
49.8%
Common
ValueCountFrequency (%)
22218
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22218
34.6%
K2105
32.8%
k2089
32.6%

s56
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing28322
Missing (%)100.0%
Memory size221.4 KiB

s57
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing28322
Missing (%)100.0%
Memory size221.4 KiB

s58
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
B
25760 
A
 
2562

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B25760
91.0%
A2562
 
9.0%

Length

2022-06-09T23:06:37.734654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.805654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b25760
91.0%
a2562
 
9.0%

Most occurring characters

ValueCountFrequency (%)
B25760
91.0%
A2562
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28322
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B25760
91.0%
A2562
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B25760
91.0%
A2562
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B25760
91.0%
A2562
 
9.0%

s59
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing28322
Missing (%)100.0%
Memory size221.4 KiB

s69
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
x
21028 
~1
6446 
C`
 
540
0
 
308

Length

Max length2
Median length1
Mean length1.246663371
Min length1

Characters and Unicode

Total characters35308
Distinct characters6
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowx
2nd rowx
3rd rowx
4th rowx
5th row~1

Common Values

ValueCountFrequency (%)
x21028
74.2%
~16446
 
22.8%
C`540
 
1.9%
0308
 
1.1%

Length

2022-06-09T23:06:37.872656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:37.952653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x21028
74.2%
16446
 
22.8%
c540
 
1.9%
0308
 
1.1%

Most occurring characters

ValueCountFrequency (%)
x21028
59.6%
~6446
 
18.3%
16446
 
18.3%
C540
 
1.5%
`540
 
1.5%
0308
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21028
59.6%
Decimal Number6754
 
19.1%
Math Symbol6446
 
18.3%
Uppercase Letter540
 
1.5%
Modifier Symbol540
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
16446
95.4%
0308
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
x21028
100.0%
Math Symbol
ValueCountFrequency (%)
~6446
100.0%
Uppercase Letter
ValueCountFrequency (%)
C540
100.0%
Modifier Symbol
ValueCountFrequency (%)
`540
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21568
61.1%
Common13740
38.9%

Most frequent character per script

Common
ValueCountFrequency (%)
~6446
46.9%
16446
46.9%
`540
 
3.9%
0308
 
2.2%
Latin
ValueCountFrequency (%)
x21028
97.5%
C540
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII35308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x21028
59.6%
~6446
 
18.3%
16446
 
18.3%
C540
 
1.5%
`540
 
1.5%
0308
 
0.9%

s70
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
op: D
24013 
op: B
 
2175
op: C
 
1985
op: A
 
149

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters141610
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowop: D
2nd rowop: D
3rd rowop: D
4th rowop: D
5th rowop: D

Common Values

ValueCountFrequency (%)
op: D24013
84.8%
op: B2175
 
7.7%
op: C1985
 
7.0%
op: A149
 
0.5%

Length

2022-06-09T23:06:38.014656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:38.084655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
op28322
50.0%
d24013
42.4%
b2175
 
3.8%
c1985
 
3.5%
a149
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o28322
20.0%
p28322
20.0%
:28322
20.0%
28322
20.0%
D24013
17.0%
B2175
 
1.5%
C1985
 
1.4%
A149
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter56644
40.0%
Other Punctuation28322
20.0%
Space Separator28322
20.0%
Uppercase Letter28322
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D24013
84.8%
B2175
 
7.7%
C1985
 
7.0%
A149
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
o28322
50.0%
p28322
50.0%
Other Punctuation
ValueCountFrequency (%)
:28322
100.0%
Space Separator
ValueCountFrequency (%)
28322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84966
60.0%
Common56644
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o28322
33.3%
p28322
33.3%
D24013
28.3%
B2175
 
2.6%
C1985
 
2.3%
A149
 
0.2%
Common
ValueCountFrequency (%)
:28322
50.0%
28322
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII141610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o28322
20.0%
p28322
20.0%
:28322
20.0%
28322
20.0%
D24013
17.0%
B2175
 
1.5%
C1985
 
1.4%
A149
 
0.1%

s71
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
b
25010 
d
 
1612
c
 
1607
a
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowb
5th rowd

Common Values

ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Length

2022-06-09T23:06:38.147656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:38.220654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Most occurring characters

ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28322
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin28322
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b25010
88.3%
d1612
 
5.7%
c1607
 
5.7%
a93
 
0.3%

n1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28321
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.20186346
Minimum2.200735999
Maximum20.88273283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:38.294653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.200735999
5-th percentile4.037448273
Q16.59212672
median9.535961455
Q314.43257994
95-th percentile17.21951518
Maximum20.88273283
Range18.68199683
Interquartile range (IQR)7.840453223

Descriptive statistics

Standard deviation4.414660192
Coefficient of variation (CV)0.4327307661
Kurtosis-1.054333729
Mean10.20186346
Median Absolute Deviation (MAD)3.501307395
Skewness0.2964904634
Sum288937.177
Variance19.48922461
MonotonicityNot monotonic
2022-06-09T23:06:38.399656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9436016952
 
< 0.1%
15.181257521
 
< 0.1%
12.436538081
 
< 0.1%
17.778950011
 
< 0.1%
6.4461663291
 
< 0.1%
6.5346494321
 
< 0.1%
7.2143696861
 
< 0.1%
10.748531291
 
< 0.1%
7.0388209861
 
< 0.1%
10.285494091
 
< 0.1%
Other values (28311)28311
> 99.9%
ValueCountFrequency (%)
2.2007359991
< 0.1%
2.2077556191
< 0.1%
2.2708709341
< 0.1%
2.3545838421
< 0.1%
2.3562444841
< 0.1%
2.3708374111
< 0.1%
2.3730414241
< 0.1%
2.3764008121
< 0.1%
2.3861271841
< 0.1%
2.391413111
< 0.1%
ValueCountFrequency (%)
20.882732831
< 0.1%
20.872894761
< 0.1%
20.828019581
< 0.1%
20.817005621
< 0.1%
20.750311241
< 0.1%
20.714725531
< 0.1%
20.674294961
< 0.1%
20.666577451
< 0.1%
20.599880531
< 0.1%
20.583987531
< 0.1%

n2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28320
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.60011114
Minimum0.311726656
Maximum3.137331325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:38.499654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.311726656
5-th percentile0.7144087329
Q11.013540084
median1.462197338
Q32.194510391
95-th percentile2.712852717
Maximum3.137331325
Range2.825604669
Interquartile range (IQR)1.180970307

Descriptive statistics

Standard deviation0.6649847397
Coefficient of variation (CV)0.4155865946
Kurtosis-1.075884152
Mean1.60011114
Median Absolute Deviation (MAD)0.528475098
Skewness0.3447461324
Sum45318.3477
Variance0.442204704
MonotonicityNot monotonic
2022-06-09T23:06:38.588658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51775372
 
< 0.1%
1.1973139772
 
< 0.1%
2.7721882621
 
< 0.1%
1.5235498311
 
< 0.1%
2.4613406781
 
< 0.1%
1.4963252761
 
< 0.1%
1.5559476321
 
< 0.1%
0.9842669361
 
< 0.1%
1.4030988161
 
< 0.1%
2.0193399941
 
< 0.1%
Other values (28310)28310
> 99.9%
ValueCountFrequency (%)
0.3117266561
< 0.1%
0.354313931
< 0.1%
0.3623995121
< 0.1%
0.3640113961
< 0.1%
0.3837833881
< 0.1%
0.39783731
< 0.1%
0.4055597631
< 0.1%
0.4060436141
< 0.1%
0.4090721031
< 0.1%
0.409644571
< 0.1%
ValueCountFrequency (%)
3.1373313251
< 0.1%
3.1331345691
< 0.1%
3.1213603021
< 0.1%
3.1114918691
< 0.1%
3.1080122851
< 0.1%
3.1077682031
< 0.1%
3.1063901321
< 0.1%
3.1047900571
< 0.1%
3.1026784951
< 0.1%
3.1007138971
< 0.1%

n3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.80509851
Minimum0
Maximum9
Zeros103
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:38.841938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.199281686
Coefficient of variation (CV)0.457697523
Kurtosis-1.210677144
Mean4.80509851
Median Absolute Deviation (MAD)2
Skewness0.1801551798
Sum136090
Variance4.836839934
MonotonicityNot monotonic
2022-06-09T23:06:38.899937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
39681
34.2%
66802
24.0%
73186
 
11.2%
82652
 
9.4%
22433
 
8.6%
41178
 
4.2%
91159
 
4.1%
1733
 
2.6%
5395
 
1.4%
0103
 
0.4%
ValueCountFrequency (%)
0103
 
0.4%
1733
 
2.6%
22433
 
8.6%
39681
34.2%
41178
 
4.2%
5395
 
1.4%
66802
24.0%
73186
 
11.2%
82652
 
9.4%
91159
 
4.1%
ValueCountFrequency (%)
91159
 
4.1%
82652
 
9.4%
73186
 
11.2%
66802
24.0%
5395
 
1.4%
41178
 
4.2%
39681
34.2%
22433
 
8.6%
1733
 
2.6%
0103
 
0.4%

n4
Real number (ℝ≥0)

Distinct28321
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.080537296
Minimum1.700369869
Maximum8.594620331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:38.978937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.700369869
5-th percentile2.173201863
Q12.833161324
median4.840320216
Q37.300738015
95-th percentile8.143224544
Maximum8.594620331
Range6.894250462
Interquartile range (IQR)4.467576692

Descriptive statistics

Standard deviation2.27616185
Coefficient of variation (CV)0.448015971
Kurtosis-1.647180832
Mean5.080537296
Median Absolute Deviation (MAD)2.285804661
Skewness0.05087975651
Sum143890.9773
Variance5.180912767
MonotonicityNot monotonic
2022-06-09T23:06:39.073939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0335730322
 
< 0.1%
2.3183846821
 
< 0.1%
2.234689861
 
< 0.1%
2.8159989931
 
< 0.1%
2.0965569691
 
< 0.1%
5.869899451
 
< 0.1%
3.9250363461
 
< 0.1%
2.1951129341
 
< 0.1%
2.6008008821
 
< 0.1%
6.261896211
 
< 0.1%
Other values (28311)28311
> 99.9%
ValueCountFrequency (%)
1.7003698691
< 0.1%
1.7737646461
< 0.1%
1.7767126371
< 0.1%
1.7794367351
< 0.1%
1.7850470251
< 0.1%
1.797218831
< 0.1%
1.8021159561
< 0.1%
1.8168752741
< 0.1%
1.8223671621
< 0.1%
1.8249952171
< 0.1%
ValueCountFrequency (%)
8.5946203311
< 0.1%
8.5944042131
< 0.1%
8.5579996781
< 0.1%
8.5182929911
< 0.1%
8.4717939891
< 0.1%
8.4654704571
< 0.1%
8.4635247971
< 0.1%
8.4627680851
< 0.1%
8.4586617451
< 0.1%
8.4579188231
< 0.1%

n5
Real number (ℝ)

HIGH CORRELATION

Distinct28290
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-32.64118301
Minimum-33.16758925
Maximum-32.28918013
Zeros0
Zeros (%)0.0%
Negative28322
Negative (%)100.0%
Memory size221.4 KiB
2022-06-09T23:06:39.175937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-33.16758925
5-th percentile-33.04085147
Q1-32.86220687
median-32.57702572
Q3-32.39673328
95-th percentile-32.33130493
Maximum-32.28918013
Range0.87840912
Interquartile range (IQR)0.4654735925

Descriptive statistics

Standard deviation0.2414528137
Coefficient of variation (CV)-0.007397183296
Kurtosis-1.243059471
Mean-32.64118301
Median Absolute Deviation (MAD)0.19516971
Skewness-0.3377380839
Sum-924463.5852
Variance0.05829946125
MonotonicityNot monotonic
2022-06-09T23:06:39.271938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32.575722082
 
< 0.1%
-32.393332962
 
< 0.1%
-32.576720982
 
< 0.1%
-32.643876232
 
< 0.1%
-32.394757822
 
< 0.1%
-32.452172682
 
< 0.1%
-32.395052892
 
< 0.1%
-32.575280452
 
< 0.1%
-32.396309762
 
< 0.1%
-32.772435332
 
< 0.1%
Other values (28280)28302
99.9%
ValueCountFrequency (%)
-33.167589251
< 0.1%
-33.147252461
< 0.1%
-33.139697611
< 0.1%
-33.136049451
< 0.1%
-33.135936551
< 0.1%
-33.135595471
< 0.1%
-33.131069411
< 0.1%
-33.130867131
< 0.1%
-33.128721651
< 0.1%
-33.128466671
< 0.1%
ValueCountFrequency (%)
-32.289180131
< 0.1%
-32.291187241
< 0.1%
-32.291752991
< 0.1%
-32.292980021
< 0.1%
-32.293333281
< 0.1%
-32.293930641
< 0.1%
-32.293980971
< 0.1%
-32.294783761
< 0.1%
-32.295375081
< 0.1%
-32.296489861
< 0.1%

n6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28299
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01501408672
Minimum0.000584433
Maximum0.029992285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:39.372937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000584433
5-th percentile0.0048245115
Q10.00919536825
median0.0160534745
Q30.0187706375
95-th percentile0.0265039274
Maximum0.029992285
Range0.029407852
Interquartile range (IQR)0.00957526925

Descriptive statistics

Standard deviation0.006550207866
Coefficient of variation (CV)0.436270816
Kurtosis-0.6052686955
Mean0.01501408672
Median Absolute Deviation (MAD)0.0044492705
Skewness0.1092154238
Sum425.2289642
Variance4.290522309 × 10-5
MonotonicityNot monotonic
2022-06-09T23:06:39.466937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0294167962
 
< 0.1%
0.0173107572
 
< 0.1%
0.0176569632
 
< 0.1%
0.0156015992
 
< 0.1%
0.0184144952
 
< 0.1%
0.0173802392
 
< 0.1%
0.0142897572
 
< 0.1%
0.0168575192
 
< 0.1%
0.0158072832
 
< 0.1%
0.0213671712
 
< 0.1%
Other values (28289)28302
99.9%
ValueCountFrequency (%)
0.0005844331
< 0.1%
0.0009770291
< 0.1%
0.0010183461
< 0.1%
0.0011771451
< 0.1%
0.0012026451
< 0.1%
0.0012239771
< 0.1%
0.0012520281
< 0.1%
0.0013009811
< 0.1%
0.0014011481
< 0.1%
0.0014299131
< 0.1%
ValueCountFrequency (%)
0.0299922851
< 0.1%
0.0299833861
< 0.1%
0.0299809891
< 0.1%
0.0299697511
< 0.1%
0.0299606541
< 0.1%
0.0299494191
< 0.1%
0.029945411
< 0.1%
0.0299443741
< 0.1%
0.0299346021
< 0.1%
0.0299298061
< 0.1%

n7
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28320
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.107428696
Minimum-9.517985619
Maximum-8.586582817
Zeros0
Zeros (%)0.0%
Negative28322
Negative (%)100.0%
Memory size221.4 KiB
2022-06-09T23:06:39.569937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9.517985619
5-th percentile-9.376176573
Q1-9.258473125
median-9.174557008
Q3-8.964786304
95-th percentile-8.719175763
Maximum-8.586582817
Range0.931402802
Interquartile range (IQR)0.2936868217

Descriptive statistics

Standard deviation0.2051999382
Coefficient of variation (CV)-0.02253105076
Kurtosis-0.6444591229
Mean-9.107428696
Median Absolute Deviation (MAD)0.114161137
Skewness0.6458019739
Sum-257940.5955
Variance0.04210701465
MonotonicityNot monotonic
2022-06-09T23:06:39.660936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.0506806722
 
< 0.1%
-9.1446231412
 
< 0.1%
-9.1260561861
 
< 0.1%
-9.300589861
 
< 0.1%
-9.2724468951
 
< 0.1%
-9.1728494881
 
< 0.1%
-9.1717397441
 
< 0.1%
-9.2597823681
 
< 0.1%
-9.2374572491
 
< 0.1%
-9.3797814611
 
< 0.1%
Other values (28310)28310
> 99.9%
ValueCountFrequency (%)
-9.5179856191
< 0.1%
-9.4962526041
< 0.1%
-9.4955974961
< 0.1%
-9.4894225981
< 0.1%
-9.4870924211
< 0.1%
-9.4866819531
< 0.1%
-9.4856670791
< 0.1%
-9.484382621
< 0.1%
-9.4841941991
< 0.1%
-9.4831555721
< 0.1%
ValueCountFrequency (%)
-8.5865828171
< 0.1%
-8.5893461441
< 0.1%
-8.5916000121
< 0.1%
-8.5920634091
< 0.1%
-8.5963262051
< 0.1%
-8.5985875461
< 0.1%
-8.5994103281
< 0.1%
-8.5995272211
< 0.1%
-8.6003418151
< 0.1%
-8.6014736121
< 0.1%

n8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28321
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.612427861
Minimum1.178469391
Maximum2.173592578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:39.753943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.178469391
5-th percentile1.409594099
Q11.489923957
median1.537262805
Q31.730249466
95-th percentile1.943883254
Maximum2.173592578
Range0.995123187
Interquartile range (IQR)0.2403255097

Descriptive statistics

Standard deviation0.1735601295
Coefficient of variation (CV)0.1076390043
Kurtosis0.1832612698
Mean1.612427861
Median Absolute Deviation (MAD)0.0742525225
Skewness0.9659096382
Sum45667.18189
Variance0.03012311854
MonotonicityNot monotonic
2022-06-09T23:06:39.850938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4723905552
 
< 0.1%
1.7322911221
 
< 0.1%
1.7626061821
 
< 0.1%
2.1587772821
 
< 0.1%
1.5351932911
 
< 0.1%
1.5729632961
 
< 0.1%
1.4960997061
 
< 0.1%
1.5104926941
 
< 0.1%
1.6972616341
 
< 0.1%
1.5220216111
 
< 0.1%
Other values (28311)28311
> 99.9%
ValueCountFrequency (%)
1.1784693911
< 0.1%
1.1866669731
< 0.1%
1.1943818411
< 0.1%
1.1991960111
< 0.1%
1.2036322061
< 0.1%
1.2070402561
< 0.1%
1.2091004571
< 0.1%
1.2186650071
< 0.1%
1.2196434321
< 0.1%
1.2294567361
< 0.1%
ValueCountFrequency (%)
2.1735925781
< 0.1%
2.1733069491
< 0.1%
2.1699125341
< 0.1%
2.1656682761
< 0.1%
2.1650439751
< 0.1%
2.1648175551
< 0.1%
2.1639586821
< 0.1%
2.1633800881
< 0.1%
2.1614918391
< 0.1%
2.1606793421
< 0.1%

n9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28321
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.232643658
Minimum2.508858399
Maximum11.2148695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:39.947937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.508858399
5-th percentile2.849423579
Q13.464229418
median4.461769905
Q36.524341464
95-th percentile9.3045669
Maximum11.2148695
Range8.706011101
Interquartile range (IQR)3.060112047

Descriptive statistics

Standard deviation2.179704833
Coefficient of variation (CV)0.4165590045
Kurtosis-0.3721391602
Mean5.232643658
Median Absolute Deviation (MAD)1.349988177
Skewness0.8337301594
Sum148198.9337
Variance4.751113159
MonotonicityNot monotonic
2022-06-09T23:06:40.033939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6534274972
 
< 0.1%
3.6985036541
 
< 0.1%
6.9293508711
 
< 0.1%
3.8853463531
 
< 0.1%
3.2322660021
 
< 0.1%
4.4767664451
 
< 0.1%
6.0106795281
 
< 0.1%
7.1198898711
 
< 0.1%
3.3939975981
 
< 0.1%
2.9693130061
 
< 0.1%
Other values (28311)28311
> 99.9%
ValueCountFrequency (%)
2.5088583991
< 0.1%
2.5145735831
< 0.1%
2.5175911761
< 0.1%
2.5228458691
< 0.1%
2.5234439561
< 0.1%
2.5335488241
< 0.1%
2.5445112181
< 0.1%
2.5481279281
< 0.1%
2.5523378581
< 0.1%
2.5537028621
< 0.1%
ValueCountFrequency (%)
11.21486951
< 0.1%
11.213377441
< 0.1%
11.185244871
< 0.1%
11.171972151
< 0.1%
11.167541281
< 0.1%
11.151870461
< 0.1%
11.133927181
< 0.1%
11.119993311
< 0.1%
11.118471481
< 0.1%
11.116793721
< 0.1%

n10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28319
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.115109134
Minimum1.173464522
Maximum12.02913485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:40.124939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.173464522
5-th percentile3.514116912
Q14.414671027
median5.31715361
Q37.027609907
95-th percentile11.41504155
Maximum12.02913485
Range10.85567033
Interquartile range (IQR)2.612938879

Descriptive statistics

Standard deviation2.474137855
Coefficient of variation (CV)0.4045942273
Kurtosis0.03692827204
Mean6.115109134
Median Absolute Deviation (MAD)1.205993773
Skewness1.073207442
Sum173192.1209
Variance6.121358126
MonotonicityNot monotonic
2022-06-09T23:06:40.219939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1065793832
 
< 0.1%
4.8145246912
 
< 0.1%
11.43125522
 
< 0.1%
5.7041869591
 
< 0.1%
4.7091673741
 
< 0.1%
6.3188041071
 
< 0.1%
6.2333810981
 
< 0.1%
4.1978734991
 
< 0.1%
5.4264956151
 
< 0.1%
3.9333344691
 
< 0.1%
Other values (28309)28309
> 99.9%
ValueCountFrequency (%)
1.1734645221
< 0.1%
1.3144926241
< 0.1%
1.6378454961
< 0.1%
1.7890318871
< 0.1%
1.7900872221
< 0.1%
1.8717740041
< 0.1%
1.9331884511
< 0.1%
1.9401497781
< 0.1%
1.9444347651
< 0.1%
1.9526496491
< 0.1%
ValueCountFrequency (%)
12.029134851
< 0.1%
12.015290491
< 0.1%
12.008082041
< 0.1%
11.997824751
< 0.1%
11.97418291
< 0.1%
11.96186391
< 0.1%
11.957590461
< 0.1%
11.953394591
< 0.1%
11.942837481
< 0.1%
11.938441421
< 0.1%

n11
Real number (ℝ≥0)

Distinct28321
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.699553124
Minimum1.500005729
Maximum1.899994972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:40.321939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.500005729
5-th percentile1.520851093
Q11.600055738
median1.698556364
Q31.799238116
95-th percentile1.879907181
Maximum1.899994972
Range0.399989243
Interquartile range (IQR)0.1991823782

Descriptive statistics

Standard deviation0.1151495872
Coefficient of variation (CV)0.06775286133
Kurtosis-1.196234084
Mean1.699553124
Median Absolute Deviation (MAD)0.0996051905
Skewness0.01257619157
Sum48134.74359
Variance0.01325942742
MonotonicityNot monotonic
2022-06-09T23:06:40.426937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5491967182
 
< 0.1%
1.5444841231
 
< 0.1%
1.7979787341
 
< 0.1%
1.8257301161
 
< 0.1%
1.8707621731
 
< 0.1%
1.7565325591
 
< 0.1%
1.8366985941
 
< 0.1%
1.6776193741
 
< 0.1%
1.8912503111
 
< 0.1%
1.7090382281
 
< 0.1%
Other values (28311)28311
> 99.9%
ValueCountFrequency (%)
1.5000057291
< 0.1%
1.5000244541
< 0.1%
1.5000390741
< 0.1%
1.5000561751
< 0.1%
1.5000766181
< 0.1%
1.5000854231
< 0.1%
1.5000956711
< 0.1%
1.500137681
< 0.1%
1.5001543691
< 0.1%
1.5001610331
< 0.1%
ValueCountFrequency (%)
1.8999949721
< 0.1%
1.8999424951
< 0.1%
1.899930941
< 0.1%
1.8999158891
< 0.1%
1.8998967751
< 0.1%
1.8998514511
< 0.1%
1.8998479231
< 0.1%
1.8998219031
< 0.1%
1.8998006571
< 0.1%
1.8997945051
< 0.1%

n12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
28039 
1
 
283

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

Length

2022-06-09T23:06:40.516937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:40.582536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

Most occurring characters

ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common28322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
028039
99.0%
1283
 
1.0%

n13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
25787 
1
 
2535

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

Length

2022-06-09T23:06:40.638539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:40.706538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

Most occurring characters

ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common28322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025787
91.0%
12535
 
9.0%

n14
Real number (ℝ≥0)

UNIQUE

Distinct28322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4975703454
Minimum0.000100503
Maximum0.999989791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:40.778537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000100503
5-th percentile0.0472238109
Q10.2478005075
median0.4952553005
Q30.7487860457
95-th percentile0.9474366258
Maximum0.999989791
Range0.999889288
Interquartile range (IQR)0.5009855382

Descriptive statistics

Standard deviation0.2892071908
Coefficient of variation (CV)0.5812388007
Kurtosis-1.199940213
Mean0.4975703454
Median Absolute Deviation (MAD)0.250563065
Skewness0.007063246395
Sum14092.18732
Variance0.08364079923
MonotonicityNot monotonic
2022-06-09T23:06:40.874537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6312203431
 
< 0.1%
0.5223642131
 
< 0.1%
0.338893191
 
< 0.1%
0.7921034051
 
< 0.1%
0.4593610311
 
< 0.1%
0.1113287221
 
< 0.1%
0.0676031241
 
< 0.1%
0.521627271
 
< 0.1%
0.6266959671
 
< 0.1%
0.2014057851
 
< 0.1%
Other values (28312)28312
> 99.9%
ValueCountFrequency (%)
0.0001005031
< 0.1%
0.0001039821
< 0.1%
0.0001460791
< 0.1%
0.0001848391
< 0.1%
0.000201431
< 0.1%
0.0002083691
< 0.1%
0.0002549211
< 0.1%
0.0002943611
< 0.1%
0.0003010481
< 0.1%
0.0003725491
< 0.1%
ValueCountFrequency (%)
0.9999897911
< 0.1%
0.9999599721
< 0.1%
0.9999451661
< 0.1%
0.9998794961
< 0.1%
0.9998644231
< 0.1%
0.9998271091
< 0.1%
0.9997368151
< 0.1%
0.9997189411
< 0.1%
0.9996937731
< 0.1%
0.999685621
< 0.1%

n15
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.997740273
Minimum0
Maximum6
Zeros4145
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size221.4 KiB
2022-06-09T23:06:40.954538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.007611091
Coefficient of variation (CV)0.6697081495
Kurtosis-1.258469508
Mean2.997740273
Median Absolute Deviation (MAD)2
Skewness-0.004129074857
Sum84902
Variance4.030502291
MonotonicityNot monotonic
2022-06-09T23:06:41.016538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
04145
14.6%
54100
14.5%
64049
14.3%
44021
14.2%
24013
14.2%
34002
14.1%
13992
14.1%
ValueCountFrequency (%)
04145
14.6%
13992
14.1%
24013
14.2%
34002
14.1%
44021
14.2%
54100
14.5%
64049
14.3%
ValueCountFrequency (%)
64049
14.3%
54100
14.5%
44021
14.2%
34002
14.1%
24013
14.2%
13992
14.1%
04145
14.6%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
23562 
1
4760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28322
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Length

2022-06-09T23:06:41.246537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-09T23:06:41.313541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Most occurring characters

ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common28322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII28322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023562
83.2%
14760
 
16.8%

Interactions

2022-06-09T23:06:33.499994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.376275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.534886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.773262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.852837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.972924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.232922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.398924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.522527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.829531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.927527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.055530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.381760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.588994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.470273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.622888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.861260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.942847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.061923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.329925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.487530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.618529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.916529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.021529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.149529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.469759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.673991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.560275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.708885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.943749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.025846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.146924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.415924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.574527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.708527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.998528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.108528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.238529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.553760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.754992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.646876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.789888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.021701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.109856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.225923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.501923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.655529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.792527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.076529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.193529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.324529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.635761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.841992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.736886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.004894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.105701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.194870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.312922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.591922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.743527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.879527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.161527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.282532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.418527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.723761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.924996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.824888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.087896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.185774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.279881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.551922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.678924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.826529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.965530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.241529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.368528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.507156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.810760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.014995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:19.915888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.173907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.271775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.372028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.638924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.767921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.915529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.216529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.333528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.458529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.600155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.899761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.097992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.001889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.258906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.353773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.458037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.720924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.853924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.998528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.302529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.416529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.548528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.843760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.984760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.182993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.090886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.346911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.435775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.543471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.804924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.944924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.083529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.391527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.500528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.634528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:31.932759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.071392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.261991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.173888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.425908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.512777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.622922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.883924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.034925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.163532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.472527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.579528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.713537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.016761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.153395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.346997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.260887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.511249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.594786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.706924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:24.967924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.121925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.250527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.561529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.664532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.795528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.104761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.236393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.437992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.358886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.603262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.686796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.800922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.059924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.218924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.348531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.656529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.760527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.886529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.200759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.329995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:34.526994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:20.445888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:21.688261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:22.768795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:23.886925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:25.147925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:26.309926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:27.433527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:28.742529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:29.843526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:30.969529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:32.290760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-09T23:06:33.413993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-09T23:06:41.391538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-09T23:06:41.546133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-09T23:06:41.702132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-09T23:06:41.853132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-09T23:06:42.004131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-09T23:06:34.783046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-09T23:06:35.419046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-09T23:06:35.680048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-09T23:06:35.972048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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1b'gAAAAABinOiWGC1WhR6WYP0DA5ssGv9rIekrWUwCdJ8FvkVcSUl2AquMfWqtOqs3AQYGxS13wQv9Tx4GEkPEl5RnbchazqsZcw=='MYY1DDB11NaNNaNNaNNaNBNaNxop: Db7.1445580.84486636.197768-32.5765970.013857-9.0982871.5058856.7913576.1104161.712354000.39274631
2b'gAAAAABinOibTcOBFIVeA4nVF3FuFz_QX3ZlPPFc21gS9EYdw6Wo8Y5agbzfD6hhsaXZCBdrUQVPpZBXYsODc2PDjER2DX5QcA=='MYY1DDB01NaNNaNNaNNaNBNaNxop: Db6.9232361.04201867.824401-32.5105440.013943-9.2348941.5038284.1096853.9532261.804260000.22253720
3b'gAAAAABinOig-g3-Q1ggjlMhfUSdn21Aj5yVVeVvXbisuGUmadvbBh5W28jivd2vgGUWVfHtMdC6vNLrDyFM5NgzILAOorgWGA=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db5.7498400.78143928.256767-32.3986790.010387-9.3780251.4858637.2658764.5594191.537645000.15440940
4b'gAAAAABinOiXdoaNUzihOSbyY1tjWtd5EgMaXkkvH6SVbyppsCh4sW4X5QGqFrLNAcfMQ4NPHOLqbNUVKU-5xxvWCwb5tT91Pw=='MNY1BDD1lNaNNaNNaNNaNBNaN~1op: Dd14.7719591.24818832.300011-32.3967460.016289-9.2619621.6192103.7376474.0520031.637831010.73756010
5b'gAAAAABinOiWbgAxe8Uy9tboiJGZEYK7zcGy6fv8_5Ao4nwN9iCZx70at4UsfDvb3X4JL1Om9_sgAPBUiuuize3v7CwcsFm6Bw=='MYY1DCB11NaNNaNNaNNaNBNaNxop: Cb11.5333972.06274992.732090-32.8655950.008230-8.8859641.84586210.66065111.7041211.568647000.68764060
6b'gAAAAABinOiYFRgmHZu5sLaAYKtwJbRhPnjCYPVxQPNfCegG_-eyGDg_9F43uYQqD9Ok9MKZlmuyQmf9LY0pk0GOZFDqZbFMIA=='MYY1BDB1lNaNNaNNaNNaNBNaN~1op: Db16.8045802.67138697.378877-32.5771930.007366-8.8180201.5353698.15503111.5554411.543350000.98384051
7b'gAAAAABinOiXQjg5rjPZsHSAQrJhP8u_21fwETW89EFNxR9FtB8Fh3PlYL5EV8PSGKzRZ0gyBPCkfS3ldV44cbP12fTatgRTFw=='MYY1BCB0lNaNNaNNaNNaNANaN~1op: Cb12.4963992.43798137.066580-32.3120620.022486-9.2493171.8111642.8228416.5905231.694829000.77779800
8b'gAAAAABinOic9rpK09lv7Q2k7bMbzXsT0ZluA8SfT7x0Zu92oMUyQExuOhSYx-UBx1JLSBOOyN9PptyKg6nAs-HIBfVBNV3wOw=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db17.6231422.28403568.069883-32.3590320.013593-9.0423501.51000210.66168611.2769861.779480000.55701900
9b'gAAAAABinOiWr-5MKlXYJ9hkIMYLh2XNHJeYWIIpt94IyPKqeNbqH9nEZdgg27APXCQ4Nzhzsc8-SqrrXSgGXEMGE3oSLzNZFw=='MYN1DDB01NaNkkNaNNaNBNaNxop: Db4.3879130.83772768.326920-32.6410600.012796-9.3260151.3839082.6718423.9403051.716165000.21287410

Last rows

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28313b'gAAAAABinOiYUZloPMZq3zhTO8zFmQI0t4iyqTifomfpGwaMUBkvsF8H1TAd2FLWnD1M3hRlf4WSyBORaU3B9n14yKF2iHMRew=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db13.4925341.71986677.920856-32.8775830.017864-8.8271751.7110093.4172938.0806161.838601000.14570660
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28319b'gAAAAABinOicscC37w1W9uiIlYL1U5D_mUtmgdUg8QTlgTDiCQj0N8l5xVuD5tTmGrYgogJLq-v9Z6fCyGeAng__ulkRJQi-Xg=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db13.4297971.80056237.763386-32.5759750.017782-9.2337221.4559112.9002496.0586811.627479010.82658710
28320b'gAAAAABinOib-JZ8iQDxjSRsa1dPT1TqeSQ_si4mAw5fX_kDmvIAkIofJCd_35viYIE5AhMTE-MYsS5yYkiD_9J6ewDjL1cYuQ=='MYY1BCB11NaNNaNNaNNaNBNaN~1op: Cb16.0946222.43828086.942766-32.5775630.007324-8.7489412.07439410.67325911.3876461.560391000.10352350
28321b'gAAAAABinOiZ3mCQAtoJFXj-Ymjhi9FbbZ_Ypi5gDoAEYe1Cx66GhkhZ5UIJNLcPlgrRyfpabvIwD5ok1GxipWfkujjwXyMMBw=='MYY1DDB1lNaNNaNNaNNaNBNaNxop: Db7.0581071.02317235.942025-32.8488970.015928-9.2809501.4904783.5695984.5849671.777995010.43507950